MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202611070661 A) filed by Dr. Lalit Johari; Jagadeesh Sundaramoorthy; Aishwarya K P; Dr. S K Hiremath; Mr. Kedar Hiremath; and Dr. C. Dinesh on June 05, 2026, for Artificial Intelligence Enabled Predictive Software Maintenance System For Runtime Fault Forecasting And Automated Remediation In Distributed Computing Environments.
Inventors include Dr. Lalit Johari; Jagadeesh Sundaramoorthy; Aishwarya K P; Dr. S K Hiremath; Mr. Kedar Hiremath; and Dr. C. Dinesh.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: 042] The present invention relates to an artificial intelligence enabled predictive software maintenance system for runtime fault forecasting and automated remediation in distributed computing environments. The system comprises a telemetry acquisition module for collecting application logs, exception traces, resource utilization metrics, response-time data, dependency records, deployment data, configuration changes, and maintenance ticket data from monitored software components. A data normalization layer converts heterogeneous data into a unified maintenance data format, and a feature engineering module generates technical maintenance features including error frequency, exception pattern, response-time deviation, memory growth rate, dependency failure ratio, restart frequency, and configuration drift score. A behavioural baseline generation module determines dynamic baseline values for each software component, and an artificial intelligence prediction engine generates fault probability values and maintenance risk scores. A root-cause inference module identifies probable technical causes, while a remediation orchestration module validates and executes or recommends controlled maintenance actions. The invention improves software reliability, reduces unplanned downtime, and enables preventive maintenance before occurrence of software failure events. Accompanied Drawing [FIGS. 1-2]
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